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| Setting | Value |
|---|---|
| Base model | Qwen/Qwen3-VL-4B-Instruct |
| Method | SFT with LoRA (r=32, alpha=16) |
| Epochs | 3 |
| Effective batch | 16 (2 × 8 grad accum) |
| Learning rate | 2e-4 (cosine, 5% warmup) |
| Precision | bf16 |
| Optimizations | LIGER kernel, fused AdamW, gradient checkpointing |
| Hardware | A10G (24GB VRAM) or A100 |
1pip install trl transformers datasets peft accelerate bitsandbytes torch torchvision \
2 trackio huggingface_hub av qwen-vl-utils Pillow liger-kernel
3
4# Login to HF Hub
5huggingface-cli login
6
7# Run training
8python train_vlm_subtask.py1from huggingface_hub import HfApi
2api = HfApi()
3# Submit as a job on A10G hardware<think>reasoning</think><answer>subtask</answer> format using stage08 rationales